A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane

Yuri Shendryk; Lecheng Pan; Matthew Craigie; Mattia Stasolla; Catherine Ticehurst; Peter Thorburn

doi:10.1109/IGARSS39084.2020.9323418

A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane

Yuri Shendryk, Lecheng Pan, Matthew Craigie, Mattia Stasolla, Catherine Ticehurst, Peter Thorburn

Remote Sensing

Agriculture and Food

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › peer review

Samenvatting

An accurate model of yield prediction will benefit many aspects of managing growth and harvest of sugarcane crops. In this study Sentinel-1 and Sentinel-2 time-series were used to automatically detect harvest dates of sugarcane fields in the Far North Queensland of Australia. Harvest date information was further used in combination with weather, soil and elevation data to predict sugarcane yield at different time steps over three consecutive growing seasons using machine learning. Our results suggest that harvest dates could be identified with detection rates of 87% and 91% using Sentinel-1 and Sentinel-2 imagery, respectively. Similarly, sugarcane yield could be predicted using Sentinel-1 and Sentinel-2 satellite imagery in conjunction with other geographical attributes with accuracy of 65% as early as 180 days after the previous harvest.

Originele taal-2	Engels
Titel	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
Uitgeverij	Institute of Electrical and Electronics Engineers Inc.
Pagina's	5167-5170
Aantal pagina's	4
ISBN van elektronische versie	9781728163741
DOI's	https://doi.org/10.1109/IGARSS39084.2020.9323418
Status	Gepubliceerd - 26 sep. 2020
Evenement	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, Verenigde Staten van Amerika Duur: 26 sep. 2020 → 2 okt. 2020

Publicatie series

Naam	International Geoscience and Remote Sensing Symposium (IGARSS)

Congres

Congres	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Land/Regio	Verenigde Staten van Amerika
Stad	Virtual, Waikoloa
Periode	26/09/20 → 2/10/20

Toegang tot document

10.1109/IGARSS39084.2020.9323418

Andere bestanden en links

Link naar publicatie in Scopus

EORegions-Science: EORegions-Science
Neyt, X. (Promotor) & Stasolla, M. (Onderzoeker)
1/11/16 → 31/08/18
Project: Onderzoek

Citeer dit

Shendryk, Y., Pan, L., Craigie, M., Stasolla, M., Ticehurst, C., & Thorburn, P. (2020). A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (blz. 5167-5170). Artikel 9323418 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS39084.2020.9323418

Shendryk, Yuri ; Pan, Lecheng ; Craigie, Matthew et al. / A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane. 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. blz. 5167-5170 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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title = "A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane",

abstract = "An accurate model of yield prediction will benefit many aspects of managing growth and harvest of sugarcane crops. In this study Sentinel-1 and Sentinel-2 time-series were used to automatically detect harvest dates of sugarcane fields in the Far North Queensland of Australia. Harvest date information was further used in combination with weather, soil and elevation data to predict sugarcane yield at different time steps over three consecutive growing seasons using machine learning. Our results suggest that harvest dates could be identified with detection rates of 87% and 91% using Sentinel-1 and Sentinel-2 imagery, respectively. Similarly, sugarcane yield could be predicted using Sentinel-1 and Sentinel-2 satellite imagery in conjunction with other geographical attributes with accuracy of 65% as early as 180 days after the previous harvest.",

keywords = "SAR, Sentinel-1, Sentinel-2, fusion, harvest date, multispectral, satellite, sugarcane, time-series, yield",

author = "Yuri Shendryk and Lecheng Pan and Matthew Craigie and Mattia Stasolla and Catherine Ticehurst and Peter Thorburn",

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Shendryk, Y, Pan, L, Craigie, M, Stasolla, M, Ticehurst, C & Thorburn, P 2020, A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane. in 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings., 9323418, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., blz. 5167-5170, 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, Virtual, Waikoloa, Verenigde Staten van Amerika, 26/09/20. https://doi.org/10.1109/IGARSS39084.2020.9323418

A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane. / Shendryk, Yuri; Pan, Lecheng; Craigie, Matthew et al.
2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. blz. 5167-5170 9323418 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › peer review

TY - GEN

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AU - Shendryk, Yuri

AU - Pan, Lecheng

AU - Craigie, Matthew

AU - Stasolla, Mattia

AU - Ticehurst, Catherine

AU - Thorburn, Peter

PY - 2020/9/26

Y1 - 2020/9/26

N2 - An accurate model of yield prediction will benefit many aspects of managing growth and harvest of sugarcane crops. In this study Sentinel-1 and Sentinel-2 time-series were used to automatically detect harvest dates of sugarcane fields in the Far North Queensland of Australia. Harvest date information was further used in combination with weather, soil and elevation data to predict sugarcane yield at different time steps over three consecutive growing seasons using machine learning. Our results suggest that harvest dates could be identified with detection rates of 87% and 91% using Sentinel-1 and Sentinel-2 imagery, respectively. Similarly, sugarcane yield could be predicted using Sentinel-1 and Sentinel-2 satellite imagery in conjunction with other geographical attributes with accuracy of 65% as early as 180 days after the previous harvest.

AB - An accurate model of yield prediction will benefit many aspects of managing growth and harvest of sugarcane crops. In this study Sentinel-1 and Sentinel-2 time-series were used to automatically detect harvest dates of sugarcane fields in the Far North Queensland of Australia. Harvest date information was further used in combination with weather, soil and elevation data to predict sugarcane yield at different time steps over three consecutive growing seasons using machine learning. Our results suggest that harvest dates could be identified with detection rates of 87% and 91% using Sentinel-1 and Sentinel-2 imagery, respectively. Similarly, sugarcane yield could be predicted using Sentinel-1 and Sentinel-2 satellite imagery in conjunction with other geographical attributes with accuracy of 65% as early as 180 days after the previous harvest.

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KW - time-series

KW - yield

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BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings

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ER -

Shendryk Y, Pan L, Craigie M, Stasolla M, Ticehurst C, Thorburn P. A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. blz. 5167-5170. 9323418. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS39084.2020.9323418

A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane

Samenvatting

Publicatie series

Congres

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Andere bestanden en links

Vingerafdruk

Projecten

EORegions-Science: EORegions-Science

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